In these day and age that data is generated at an unprecedented rate it is very hard for a human operator to analyze large bodies of data in order to extract the real information, the knowledge therein, spot a novelty, and using them to further advance the state of knowledge or discovery of a real knowledge about a subject matter.
For example for any topic or subject there are vast amount of textual, or convertible to textual characters, repositories such as collection of research papers in any particular topic or subject, images, news feeds, interviews, talks, video collections, corporate databases, surveillance pictures and videos, and the like. Gaining any benefit from such unstructured collections of information needs lots of expertise, time, and many years of training just even to separate the facts and extract value out of these immense amounts of data. Not every piece of data is worthy of attention and investigation or investment of expensive times of experts and professionals or data processing resources.
Moreover, there is no guarantee that a human investigator or researcher can accurately analyze the vast collection of documents, data, and information. The results of the investigations are usually biased by the individual's knowledge, experiences, and background. The complexities of relations in the bodies of data limit the throughputs of knowledge-based professionals and the speed at which credible knowledge can be produced. The desired speed or rate of knowledge discovery apparently is much higher than the present rate of knowledge discovery and production.